This invention relates to a rule processing system or method that provides automatic decision support, but more specifically, to an improvement that enables automatic selection or identification of rule inputs based on an initial input supplied by a user.
During automated decision support, a user may input one or more selections of rule parameters in order to attain satisfiability of a business or engineering rule, such as product configuration rule or specifications for an engineering system. Generically, user selections may take the form of selected enumeration values of attributes that characterize the rule. In a product configuration rule for a desktop computer system, for example, an attribute may comprise bundle type and selectable enumerations of that attribute may comprise Multimedia, Power PC, Business Workstation, or Entry Level. Depending on an initial selection of bundle type, enumerations of other product attributes (e.g., CPU speed, DVD speed, Hard Drive Capacity, RAM memory size, etc.) may or may not be compatible.
In order to lessen the amount of effort required of the user to select appropriate enumerations of other attributes once other attributes are selected, it is desirable to provide the user with automatic selections or identification of enumerations for the other product attributes, i.e., to automatically identify or suggest compatible inputs that satisfy the product configuration rule based on the user's manually-supplied inputs. In other words, it is desirable to automatically identify enumerations that are valid with each other and also valid with previous selections made by the user. Such automatically-supplied advice guides the user in choosing correct enumerations that reside in various valid combinations of attributes and enumerations and speeds attainment of rule compliance. In addition to product configuration, such automatic selection of enumerations may be applied to any other type of business or engineering rule processing system or method.
In the related disclosures over which the present invention is an improvement, the rule being automated is modeled by a zero-suppressed binary decision diagram (ZDD), but may also take the form of BDDs (binary decision diagrams) or DAGs directed acyclic diagrams). Using a ZDD rule model, the user's inputs are converted to a traversal ZDD which is used to traverse the rule model in order to produce an indication of satisfiability as well as conflict and select advice. Conflict and selection advice informs the user which entries invoke compliance and which entries invoke noncompliance after the user has made his or her selections of enumerations. Based on the advice, the user may change the selections according to desired configuration or other conditions.